Displaying similar documents to “The foundations of probability with black swans.”

On the foundations of statistics and decision theory.

José M. Bernardo, Javier Girón (1983)

Trabajos de Estadística e Investigación Operativa

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An elementary axiomatic foundation for decision theory is presented at a general enough level to cover standard applications of Bayesian methods. The intuitive meaning of both axioms and results is stressed. It is argued that statistical inference is a particular decision problem to which the axiomatic argument fully applies.

Comment on "On some statistical paradoxes and non-conglomerability" by Bruce Hill.

Isaac Levi (1981)

Trabajos de Estadística e Investigación Operativa

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Those who follow Harold Jeffreys in using improper priors together with likelihoods to determine posteriors have thought of the improper measures as probability measures of a deviant sort. This is a mistake. Probability measures are finite measures. Improper distributions generate σ-finite measures. (...)

Non additive ordinal relations representable by lower or upper probabilities

Andrea Capotorti, Giulianella Coletti, Barbara Vantaggi (1998)

Kybernetika

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We characterize (in terms of necessary and sufficient conditions) binary relations representable by a lower probability. Such relations can be non- additive (as the relations representable by a probability) and also not “partially monotone” (as the relations representable by a belief function). Moreover we characterize relations representable by upper probabilities and those representable by plausibility. In fact the conditions characterizing these relations are not immediately deducible...

Null events and stochastical independence

Giulianella Colleti, Romano Scozzafava (1998)

Kybernetika

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In this paper we point out the lack of the classical definitions of stochastical independence (particularly with respect to events of 0 and 1 probability) and then we propose a definition that agrees with all the classical ones when the probabilities of the relevant events are both different from 0 and 1, but that is able to focus the actual stochastical independence also in these extreme cases. Therefore this definition avoids inconsistencies such as the possibility that an event A ...